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Instructors
Kaitlin Hagan, Instructor - Probability and Statistics for Data Science with R

Kaitlin Hagan

Kaitlin Hagan received a Masters in Public Health from Columbia University and a Doctorate of Science in Epidemiology from Harvard University. She is currently a post-doctoral fellow at Brigham and Women's Hospital and Harvard Medical School in Boston, MA. Kaitlin works with the Nurses' Health Study and her research focuses on cardiovascular epidemiology and the epidemiology of aging. Kaitlin has been involved with Introductory Statistics courses at Columbia University, Harvard University, and the Harvard T.H. Chan School of Public Health and has received numerous teaching awards and citations for her work.
Michael Parzen, Instructor - Probability and Statistics for Data Science with R

Michael Parzen

Michael has over 23 years of experience as an academic. He is currently a Senior Lecturer in Statistics at Harvard University. His past experience includes being an Associate Professor of Statistics and Econometrics at Chicago Booth School of Business. He holds a PhD in Biostatistics from Harvard, an M.S. in Applied Mathematics from Brown, and a B.S. in Mathematics from Carnegie Mellon University.

Instructors: Kaitlin Hagan,  Michael Parzen

From Basic to Advanced Applied Statistics using R

  • Harvard faculty teaches you how to apply statistical methods to explore, summarize, make inferences from complex data and develop quantitative models to assist business decision making.
  • Course includes instructional component, R tutorial videos, and exercises to reinforce concepts and give you an opportunity to see statistics in action.
  • Michael Parzen is an award-winning faculty member at Harvard and teaches one of the most popular classes. Kaitlin Hagan is a post-doctoral fellow at Brigham and Women's Hospital and has won numerous teaching awards and citations for her work.

Course Description

As you may or may not know, statistics can be dangerous. They can be used to manipulate public opinion, sell you products or services you don't need, change policy decision, and significantly affect our lives. Therefore, it is important to view statistics with a critical and knowledgeable eye. We need to understand what statistics mean, how they are generated, and what their limitations are, to know when they are appropriate and when they are not. This online statistics course will introduce you to the discipline of statistics as a science of understanding and analyzing data. If you want to simultaneously learn R and applied statistics, you can count on this course to take you from beginner to an advanced level! Start off with the basics of R and exploring data, then move on to understanding uncertainty, confidence intervals and hypothesis testing, then modeling with simple and multiple regression to learn how to use data to make inferences and conclusions about real world phenomena. What is R? R is a popular programming language for statistical computing, and has seen an exponential growth in adaptation in recent years. R is growing rapidly because you can use it for free and users can submit new packages to expand functionality. You can use text interfaces or download your own graphical interfaces making R highly customizable! Learning how to use R will give you a great long term advantage for any of your work in statistics or data science as R continues to grow worldwide! UNITS Each Unit includes an instructional component, R tutorial videos, and Section exercises to give you an opportunity to see statistics in action and to help you learn how to apply the statistics to answer a real world question. Each Section contains several practice problems to reinforce concepts from the Units and demonstrate how R can be used to solve real word statistical problems.

What am I going to get from this course?

This course will introduce you to the discipline of statistics as a science of understanding and analyzing data. If you want to simultaneously learn R and applied statistics, you can count on this course to take you from beginner to an advanced level.

Prerequisites and Target Audience

What will students need to know or do before starting this course?

  • We will be using a custom created text that will be available when you sign up for the course.
  • You will need to install R and RStudio onto the computer you will be using for the course. Instructions for downloading the software can be found on the course website.

Who should take this course? Who should not?

  • This course assumes knowledge of just basic high school algebra. No programming experience is required, but you should be comfortable downloading and installing R (its free) from the web. This course is designed for students of all levels, and methodically moves from basic to more complex ideas in each section. Material is presented in such a way that even people who are unfamiliar with programming and command prompts can follow along.

Curriculum

Module 1: Graphing and Summarizing Data

Lecture 1 Section 0: Introduction to R
06:53
Lecture 2 Graphing and Summarizing Data Part 1
13:32

Lecture 3 Graphing and Summarizing Data Part 2
14:02

Lecture 4 Graphing and Summarizing Data: Section Video and Additional Practice Problems
07:18

Module 2: Measures of Association and Regression

Lecture 5 Measures of Association and Regression Part 1
18:03
Lecture 6 Measures of Association and Regression Part 2
18:33
Lecture 7 Measures of Association and Regression: Section Video and Additional Practice Problems
07:14

Module 3: Collecting Data

Lecture 8 Collecting Data Part 1
17:02

Lecture 9 Collecting Data Part 2
12:44
Lecture 10 Collecting Data: Section Video and Additional Practice Problems
07:04

Module 4: Basic Probability

Lecture 11 Basic Probability Part 1
22:18
Lecture 12 Basic Probability Part 2
18:11
Lecture 13 Basic Probability Part 3
06:04
Lecture 14 Basic Probability: Section Video and Additional Practice Problems
09:02

Module 5: Random Variables

Lecture 15 Random Variables Part 1
09:47
Lecture 16 Random Variables Part 2
06:23
Lecture 17 Random Variables Part 3
07:55
Lecture 18 Random Variables Part 4
12:27
Lecture 19 Random Variables: Section Video and Additional Practice Problems
18:00

Module 6: Sampling Distributions

Lecture 20 Sampling Distributions Part 1
17:07
Lecture 21 Sampling Distributions Part 2
03:40
Lecture 22 Sampling Distributions Part 3
02:39
Lecture 23 Sampling Distributions Part 4
05:07
Lecture 24 Sampling Distributions: Section Video and Additional Practice Problems
07:23

Module 7: Confidence Intervals

Lecture 25 Confidence Intervals Part 1
09:18
Lecture 26 Confidence Intervals Part 2
08:08
Lecture 27 Confidence Intervals Part 3
10:09
Lecture 28 Confidence Intervals Part 4
10:03
Lecture 29 Confidence Intervals: Section Video and Additional Practice Problems
06:15

Module 8: Hypothesis Testing

Lecture 30 Hypothesis Testing Part 1
19:48
Lecture 31 Hypothesis Testing Part 2
14:22
Lecture 32 Hypothesis Testing Part 3
09:28
Lecture 33 Hypothesis Testing Part 4
19:36
Lecture 34 Hypothesis Testing: Section Video and Additional Practice Problems
07:21

Module 9: Analysis of Two-way Tables

Lecture 35 Analysis of Two-way Tables Part 1
15:24
Lecture 36 Analysis of Two-way Tables Part 2
11:27
Lecture 37 Analysis of Two-way Tables: Section Video and Additional Practice Problems
02:59

Module 10: Inference for Regression

Lecture 38 Inference for Regression Part 1
16:58
Lecture 39 Inference for Regression Part 2
15:17
Lecture 40 Inference for Regression: Section Video and Additional Practice Problems
05:38

Reviews

10 Reviews

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Sharon V

December, 2016

I really like the different components of this course! Instruction video, tutorial videos and a great exercise to finish it off. That’s how I like to learn.

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Peter G

December, 2016

Amazing course! Instructor is really great!

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Zhen W

May, 2017

A nice course with both the instructors sharing their amazing knowledge with the participants from basics to advanced applied statistics with R. I feel it is a good opportunity to anyone to learn fro the Harvard faculty without ever enrolling with the Harvard university. This course with educational and interesting instruction components, videos for R tutorial and exercise definite give opportunity to a serious student to see statistics in action. In the course it was very well explained the importance of statistics, how they are generated and their limitations and when appropriate and when not.

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Jay J

May, 2017

The learning process in this course is so much interactive with video tutorials and exercises from the highly qualified instructors. It was interesting to learn how to use data to make inferences and conclusions about real world phenomena.

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Benjamin C

May, 2017

Thanks to this excellent course, as a paramedic professional, I could learn much more to apply probability and statistics in my field. Learning how to use R was great

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Erin W

July, 2017

This is a highly recommended course. Great overview of statistics and fantastic starting point.

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Nick R

July, 2017

A detailed course with both tutors sharing their incredible information with the participants from principles to advanced applied statistics with R.

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Alexander K

July, 2017

I appreciated the finding of this course. Walked away with exactly what I was hoping for.

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Pavan G

July, 2017

This course provided me with a practical learning experience. The course was formatted impressively and easy to follow/understand. Provided great detail.

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Edward C

November, 2017

This course provides a complete overview of statistics including data collection, statistical inference, estimation and testing methods, and basic modeling techniques. You'll learn how to use R, an important statistical software tool. By the end of the course, you'll be able to perform basic statistical analyses and have the base knowledge to take more advanced Statistics classes.

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